monash_81873.pdf (1.14 MB)
Non-parametric estimation of forecast distributions in non-linear, non-gaussian state space models
thesis
posted on 2017-02-06, 05:48 authored by Ng, Jason Wei JianNon-Gaussian time series variables are prevalent in the economic and finance spheres, with state space models often employed to analyze such variables and, ultimately, to produce forecasts. A review of the relevant literature reveals that existing methods are characterized by a reliance on (potentially incorrect) parametric assumptions and are often computationally expensive. The primary aim of this thesis is to develop a non-parametric approach to forecasting - within the state space framework - with computational ease an important focus. With a view to capturing all relevant information about the likely future values of the variable of interest, the approach is used to produce non-parametric estimates of the full forecast distribution over any time horizon.
Simulation experiments are used to document the accuracy of the non-parametric method relative to both correctly and incorrectly specified parametric alternatives, in a variety of relevant settings. Applying a range of methods for evaluating and comparing distributional forecasts, the non-parametric method is shown to perform significantly better, overall, than misspecified parametric alternatives while remaining competitive with correctly specified parametric estimators.
Focus is then given to the development of a new non-Gaussian state space model for observed realized volatility from which estimates of forecast distributions of future volatility are produced using the non-parametric method. In an empirical illustration, the non-parametric method is used to produce sequential estimates of the out-of-sample one-step-ahead forecast distribution of realized volatility on the S&P500 index during the recent financial crisis. A resampling technique for measuring sampling variation in an estimated forecast distribution is also demonstrated.
The proposed filtering algorithm is further extended to cater, in particular, for multi-step-ahead forecasting and multivariate systems. A simulation-based version of the algorithm is also illustrated, with the algorithm in this form seen to be a computationally efficient alternative to existing particle filtering algorithms.
History
Campus location
AustraliaPrincipal supervisor
Gae MartinAdditional supervisor 1
Catherine ForbesYear of Award
2012Department, School or Centre
Econometrics and Business StatisticsCourse
Doctor of PhilosophyDegree Type
DOCTORATEFaculty
Faculty of Business and EconomicsUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC